python如何使for循环实现和列表推导式一样的输出形式?见图
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所以,图呢 临时号 发表于 2022-9-16 17:09
所以,图呢
我是一串葡萄 发表于 2022-9-16 17:20
a=['123','33211','12321','13531']
res=[]
for each in a:
if each[::-1]==each:
res.append(each)
print(res) 临时号 发表于 2022-9-16 17:24
https://fishc.com.cn/forum.php?mod=image&aid=159968&size=300x300&key=634f1757565ff656&nocache=yes&type=fixnone
我是一串葡萄 发表于 2022-9-16 18:50
是直接复制我的吗,如果是的就自己重抄一遍,如果不是就直接复制我代码
报错信息是缩进错误 我是一串葡萄 发表于 2022-9-16 18:50
我这边运行没有问题
页:
[1]